In this paper, we have introduced a novel high framerate video synthesis framework by fusing intensity frames with event streams, taking advantages from both ends. Our framework includes two key steps, i.e., DMR and RD. Our DMR is free of training and is capable to unify different fusion settings between the two sensing modalities, which was not considered in previous work such as [1]}, [2]}. We show in real data that our DMR performs better than existing algorithms. We show in simulation that a RD can be trained to effectively remove artifacts from DMR. Currently we train an RD from single-frame prediction case. It is interesting to further augment the training samples with all the cases, which we will investigate in the future. Applying our RD to real data faces a domain gap due to the resolution (both spatial and temporal) and noise level mismatch. Currently, none of the existing DAVIS datasets contains enough sharp intensity images captured at high speed for training/fine-tuning. We will investigate event simulation using event simulator [3]} in our future work.
